Which of the following best describes "outliers" in data?

Outliers are best described as data points that significantly differ from other observations. These points stand out from the rest of the dataset due to their extreme values, which can be either much higher or much lower than the other data points. Identifying outliers is crucial in data analysis, as they can provide insights into variability in the data, indicate potential measurement errors, or highlight unusual phenomena worth investigating further.

The nature of outliers often leads analysts to carefully assess their impact on statistical calculations, such as means and standard deviations. In some cases, outliers can reveal important trends or anomalies within the dataset while, at other times, they may skew results if not appropriately addressed.

The other options do not accurately define outliers. Options describing data points that are consistent with trends, represent average values, or confirm established patterns indicate typical or expected observations rather than the unusual nature of outliers. Hence, the correct characterization emphasizes the distinct variance that outliers represent within the context of the data analysis process.